More Science or Ethnics in
Economics and Finance?
Rosy picture of a better world
• Nowadays the military plus civilian deaths rate
does not even touch the historical low point,
not to mention the absolute number due to
population growth!
• It’s about fact, not scientism, my friends.
Annual estimates of battle-related deaths worldwide, 1989-2013
(data source: UCDP)
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UCDP Uppsala Conflict Data Program
Number of Conflicts
http://ucdp.uu.se/#/encyclopedia
Number of Deaths
http://ucdp.uu.se/#/exploratory
Wage our hope on Science
• Before Fukushima, Japanese scientists and
engineers assured that disaster like this would
never happen. In the name of Technology and
Science!
• Black-Scholes model
• 1998 Long Term Capital Management
Derivatives: Don Quixote-like (or smart ass’)
endeavor to conquer uncertainty
• Derivatives are not transactions in shares of stock or interest
rates, in human lives, in houses vulnerable to fire, or in home
mortgages. The product in derivative transaction is
uncertainty itself. (Peter L. Bernstein Against The Gods 314)
• The financial solvency of these institutions supports the
financial solvency of the world economic system itself. Every
single day, they are involved in millions of transactions
involving trillions of dollars in a complex set of arrangements
whose smooth functioning is essential. The margin for error is
miniscule. Poor controls over the size and diversification of
exposures are intolerable when the underlying volatility of the
derivatives is so high and when so much is at stake beyond
the fortunes of any single institution. (Bernstein 327)
We cannot enter data about the future into the computer because such data
are inaccessible to us. So we pour in data from the past to fuel the decisionmaking mechanisms created by our models, be they linear or nonlinear. But
therein lies the logician’ s trap: past data from real life constitute a sequence
of events rather than a set of independent observations, which is what the
laws of probability demand. History provides us with only one sample of the
economy and the capital markets, not with thousands of separate and
randomly distributed numbers. Even though many economic and financial
variables fall into distributions that approximate a bell curve, the picture is
never perfect. Once again, resemblance to truth is not the same as truth. It is
in those outliers and imperfections that the wildness lurks.
Finally, the science of risk management sometimes creates new risks even as
it brings old risks under control. Our faith in risk management encourages us
to take risks we would not otherwise take…Derivative financial instruments
designed as hedges have tempted investors to transform them into
speculative vehicles with sleigh-rides for payoffs and involving risks that no
corporate risk manager should contemplate.
(Bernstein 335)
Basel II: risk management
Three pillars: (1) risk-sensitive minimum capital
requirements, (2) supervisory review and (3) market
discipline.
Quantifiable risks: credit risk, operational risk, market risk
An OECD study suggests that bank regulation based on the Basel
accords encourage unconventional business practices and
contributed to or even reinforced adverse systemic shocks that
materialised during the financial crisis. According to the study,
capital regulation based on risk-weighted assets encourages
innovation designed to circumvent regulatory requirements and
shifts banks' focus away from their core economic functions.
(wikipedia, “Basel II”)
Value at Risk: risk measurement
VaR has been controversial since it moved from trading desks into the public eye
in 1994. Nassim Taleb claimed VaR:
• Ignored 2,500 years of experience in favor of untested models built by nontraders
• Was charlatanism because it claimed to estimate the risks of rare events,
which is impossible
• Gave false confidence
• Would be exploited by traders
In 2008 David Einhorn in Global Association of Risk Professionals Review
compared VaR to "an airbag that works all the time, except when you have a
car accident." He further charged that VaR:
• Led to excessive risk-taking and leverage at financial institutions
• Focused on the manageable risks near the center of the distribution and
ignored the tails
• Created an incentive to take "excessive but remote risks"
• Was "potentially catastrophic when its use creates a false sense of security
among senior executives and watchdogs.“
(Wikipedia ‘Value at Risk’)
Recipe for Disaster
Li's formula, known as a Gaussian copula function, looked
like an unambiguously positive breakthrough, a piece of
financial technology that allowed hugely complex risks to
be modeled with more ease and accuracy than ever before.
With his brilliant spark of mathematical legerdemain, Li
made it possible for traders to sell vast quantities of new
securities, expanding financial markets to unimaginable
levels.
Felix Salmon, ‘Recipe for Disaster: The Formula That Killed Wall Street,’ Wired, 23.2.09
Probability
Survival times
Equality
Specifically, this is a joint default
probability—the likelihood that
any two members of the pool (A
and B) will both default. It's what
investors are looking for, and the
rest of the formula provides the
answer.
The amount of time between now
and when A and B can be expected
to default. Li took the idea from a
concept in actuarial science that
charts what happens to someone's
life expectancy when their spouse
dies.
A dangerously precise concept,
since it leaves no room for error.
Clean equations help both quants
and their managers forget that the
real world contains a surprising
amount of uncertainty, fuzziness,
and precariousness.
Copula
Distribution functions
Gamma
This couples (hence the Latinate
term copula) the individual
probabilities associated with A and
B to come up with a single number.
Errors here massively increase the
risk of the whole equation blowing
up.
The probabilities of how long A
and B are likely to survive. Since
these are not certainties, they can
be dangerous: Small
miscalculations may leave you
facing much more risk than the
formula indicates.
The all-powerful correlation
parameter, which reduces
correlation to a single constant—
something that should be highly
improbable, if not impossible. This
is the magic number that made Li's
copula function irresistible.
Smart Ass fooling with probability
From attempt to manage risk to blatantly ignore risk
According to these financial models:
• LTCM 7-sigma event: happens once in 30 billion year.
• 1987 October Black Monday 10-sigma event: unlikely to
happen in a billion cycles of the history of the universe
• 20-sigma event: ten times the total number of elemental
particles in the universe
• Subprime crisis 25-sigma event: add 52 zero to the 20-sigma
event, chance of winning 21 UK Lotto prices in a row
From John Lanchester, Whoops!! Chap. 5
Financial Engineering or Profiteering?
George Soros: “Financial engineers claimed they were
reducing risks through geographic diversification:
in fact they were increasing them by creating an
agency problem. The agents were more interested
in maximizing fee income than in protecting the
interests of bondholders. … Custom-made
derivatives only serve to improve the profit margin
of the financial engineers designing them.”
• Science is not the source of disaster. Scientism
is.
Global Regime of Expulsion
Forced Displacement
32.4 million
Some economic genocide “craps”
IMF, World Bank Structural Condionalities
Estimated 2 million (The Unholy Trinity)
Russia
Nutritional/biological Expulsion
795 million people in the world do not have
enough food to lead a healthy active life. WFO
Including under-nutrition, micro malnutrition:
• 1 Billion (Raj Patel, Stuffed and Starved)
A New Theory of Value
• Go back to Marx in order to transcend Marx
• Use Value/Exchange Value
• Interactive Value
Rethink money as a form of power: command or
claim to social wealth
• We Need More Ethnics not Science
in Economics and Studies of Finance